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Deep Learning Systems


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Table of Contents

  • Preface
  • Acknowledgments
  • Introduction
  • Building Blocks
  • Models and Applications
  • Training a Model
  • Distributed Training
  • Reducing the Model Size
  • Hardware
  • Compiler Optimizations
  • Frameworks and Compilers
  • Opportunities and Challenges
  • Bibliography
  • Author's Biography

About the Author

Andres Rodriguez is a Sr. Principal Engineer and AI Architect in the Data Platform Group at Intel Corporation where he designs deep learning solutions for Intel's customers and provides technical leadership across Intel for deep learning hardware and software products. He has 15 years of experience working in AI. Andres received a Ph.D. from Carnegie Mellon University for his research in machine learning. He was the lead instructor in the Coursera course An Introduction to Practical Deep Learning to over 20 thousand students. He has been an invited speaker at several AI events, including AI with the Best, ICML, CVPR, AI Frontiers Conference, Re-Work Deep Learning Summit, TWIML, Startup MLConf, Open Compute Platform Global Summit, AWS re: Invent, Baidu World, Baidu Cloud ABC Inspire Summit, Google Cloud OnAir Webinar, and several Intel events, as well as an invited lecturer at Carnegie Mellon University, Stanford University, UC Berkeley, and Singularity University.

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